---
library_name: setfit
tags:
- setfit
- absa
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Gamenya:Gamenya oke bagus saya suka, yg saya tidak suka joystick nya pindah²
ga bsa netep disatu tempat jadi pada saat mau gerak suka susah nyangkut dan ga
terbiasa dg joystick yg bisa pindah²
- text: 'game:kekurangan game ini, 1-) PETI terbatas : saya berharap ini diubah menjadi
seperti CLASH ROYALE, karena koin di game ini tidak bisa didapat setiap waktu,
Kecuali top up. 2-) Tier/rank : tolong di tambah sistem rank, karena sistem rank
akan membuat banyak player bersaing dan menambah keseruan karna ada tantangan
( seperti Clash Royale ).. 3-) Sinyal & bug ( sinyal mendadak lemah dan gk bisa
masuk pertandingan ) : karena game ini masih baru jadi wajar, tapi tolong diperbaiki
untuk kenyamanan pemain'
- text: diam:Gamenya sih udah bagus, Grafik juga bagus, pertempurannya juga udah bagus
dan menarik, tapi ada masalah yang bikin kesel nih game yaitu analognya ngikut
gak bisa di setting jadi diam aja, itu bikin gak nyaman banget sih buat gameplaynya.
- text: analognya:Kekurangan game ini peti nya terbatas dan tolong adakan setingan
analognya supaya fix posisi nya dan tolong di permudah dapat goldnya, over all
game ini udah bagus
- text: gara gara analog:masalah analog yang belum anda perbaiki memberikan pengalaman
geme yang buruk jika Dev ingin memperbaiki masalah bug analog saya akan memberikan
5 bintang win strike saya terpecah cuma gara gara analog ini kanjud
pipeline_tag: text-classification
inference: false
---
# SetFit Aspect Model
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
This model was trained within the context of a larger system for ABSA, which looks like so:
1. Use a spaCy model to select possible aspect span candidates.
2. **Use this SetFit model to filter these possible aspect span candidates.**
3. Use a SetFit model to classify the filtered aspect span candidates.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **spaCy Model:** id_core_news_trf
- **SetFitABSA Aspect Model:** [Funnyworld1412/ABSA_game_squad_busters-aspect](https://huggingface.co/Funnyworld1412/ABSA_game_squad_busters-aspect)
- **SetFitABSA Polarity Model:** [Funnyworld1412/ABSA_game_squad_busters-polarity](https://huggingface.co/Funnyworld1412/ABSA_game_squad_busters-polarity)
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| aspect | - 'koneksi:koneksi tidak stabil, setelah matching, tiba tiba lag dan tidak bisa masuk kedalam game, saya coba digame lain koneksi aman, tapi waktu balik lagi ke game ini masih aja lag logo wifi, dan parahnya lagi setelah lag didalam match, tidak bisa melanjutkanya lagi sistem eror mohon muat ulang game, begitu...terus hingga match berakhir. mohon lakukan perbaikan atas lag signal tidak stabil'
- 'game:gamenya sebenarnya sangat seru, konsepnya menarik,, gameplay simple. yang membuat jengkel adalah masalah koneksi yang tiba down atau bahkan terputus, dan yang membuat kesal winstreak yang dikumpulkan hilang hanya karena server yang tidak setabil,, padahal koneksi di rumah sangat lancar.. kemarin ada maintenance kirain mau perbaiki masalah ini, ternyata tetap saja dan malah tidak ada kompensasi, mungkin juga karena game baru jadi butuh lebih banyak persiapan,, semoga masalahnya cepat diperbaiki.'
- 'konsepnya:gamenya sebenarnya sangat seru, konsepnya menarik,, gameplay simple. yang membuat jengkel adalah masalah koneksi yang tiba down atau bahkan terputus, dan yang membuat kesal winstreak yang dikumpulkan hilang hanya karena server yang tidak setabil,, padahal koneksi di rumah sangat lancar.. kemarin ada maintenance kirain mau perbaiki masalah ini, ternyata tetap saja dan malah tidak ada kompensasi, mungkin juga karena game baru jadi butuh lebih banyak persiapan,, semoga masalahnya cepat diperbaiki.'
|
| no aspect | - 'koneksi:koneksi tidak stabil, setelah matching, tiba tiba lag dan tidak bisa masuk kedalam game, saya coba digame lain koneksi aman, tapi waktu balik lagi ke game ini masih aja lag logo wifi, dan parahnya lagi setelah lag didalam match, tidak bisa melanjutkanya lagi sistem eror mohon muat ulang game, begitu...terus hingga match berakhir. mohon lakukan perbaikan atas lag signal tidak stabil'
- 'matching:koneksi tidak stabil, setelah matching, tiba tiba lag dan tidak bisa masuk kedalam game, saya coba digame lain koneksi aman, tapi waktu balik lagi ke game ini masih aja lag logo wifi, dan parahnya lagi setelah lag didalam match, tidak bisa melanjutkanya lagi sistem eror mohon muat ulang game, begitu...terus hingga match berakhir. mohon lakukan perbaikan atas lag signal tidak stabil'
- 'lag:koneksi tidak stabil, setelah matching, tiba tiba lag dan tidak bisa masuk kedalam game, saya coba digame lain koneksi aman, tapi waktu balik lagi ke game ini masih aja lag logo wifi, dan parahnya lagi setelah lag didalam match, tidak bisa melanjutkanya lagi sistem eror mohon muat ulang game, begitu...terus hingga match berakhir. mohon lakukan perbaikan atas lag signal tidak stabil'
|
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import AbsaModel
# Download from the 🤗 Hub
model = AbsaModel.from_pretrained(
"Funnyworld1412/ABSA_game_squad_busters-aspect",
"Funnyworld1412/ABSA_game_squad_busters-polarity",
)
# Run inference
preds = model("The food was great, but the venue is just way too busy.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 6 | 42.9092 | 90 |
| Label | Training Sample Count |
|:----------|:----------------------|
| no aspect | 2181 |
| aspect | 506 |
### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.3499 | - |
| 0.0037 | 50 | 0.2258 | - |
| 0.0074 | 100 | 0.1438 | - |
| 0.0112 | 150 | 0.3667 | - |
| 0.0149 | 200 | 0.2931 | - |
| 0.0186 | 250 | 0.3144 | - |
| 0.0223 | 300 | 0.1334 | - |
| 0.0261 | 350 | 0.0919 | - |
| 0.0298 | 400 | 0.3432 | - |
| 0.0335 | 450 | 0.2318 | - |
| 0.0001 | 1 | 0.2543 | - |
| 0.0037 | 50 | 0.2765 | - |
| 0.0074 | 100 | 0.254 | - |
| 0.0112 | 150 | 0.0406 | - |
| 0.0149 | 200 | 0.0707 | - |
| 0.0186 | 250 | 0.0344 | - |
| 0.0223 | 300 | 0.0112 | - |
| 0.0261 | 350 | 0.4567 | - |
| 0.0298 | 400 | 0.2479 | - |
| 0.0335 | 450 | 0.0487 | - |
| 0.0372 | 500 | 0.1762 | - |
| 0.0409 | 550 | 0.1578 | - |
| 0.0447 | 600 | 0.319 | - |
| 0.0484 | 650 | 0.0443 | - |
| 0.0521 | 700 | 0.42 | - |
| 0.0558 | 750 | 0.1629 | - |
| 0.0595 | 800 | 0.2677 | - |
| 0.0633 | 850 | 0.0027 | - |
| 0.0670 | 900 | 0.2256 | - |
| 0.0707 | 950 | 0.0044 | - |
| 0.0744 | 1000 | 0.0248 | - |
| 0.0782 | 1050 | 0.0387 | - |
| 0.0819 | 1100 | 0.0129 | - |
| 0.0856 | 1150 | 0.0867 | - |
| 0.0893 | 1200 | 0.0801 | - |
| 0.0930 | 1250 | 0.1524 | - |
| 0.0968 | 1300 | 0.3153 | - |
| 0.1005 | 1350 | 0.1654 | - |
| 0.1042 | 1400 | 0.0051 | - |
| 0.1079 | 1450 | 0.0131 | - |
| 0.1116 | 1500 | 0.0052 | - |
| 0.1154 | 1550 | 0.0153 | - |
| 0.1191 | 1600 | 0.1445 | - |
| 0.1228 | 1650 | 0.0005 | - |
| 0.1265 | 1700 | 0.0021 | - |
| 0.1303 | 1750 | 0.3321 | - |
| 0.1340 | 1800 | 0.1726 | - |
| 0.1377 | 1850 | 0.3157 | - |
| 0.1414 | 1900 | 0.0264 | - |
| 0.1451 | 1950 | 0.2539 | - |
| 0.1489 | 2000 | 0.1556 | - |
| 0.1526 | 2050 | 0.0294 | - |
| 0.1563 | 2100 | 0.1472 | - |
| 0.1600 | 2150 | 0.0203 | - |
| 0.1638 | 2200 | 0.2612 | - |
| 0.1675 | 2250 | 0.0182 | - |
| 0.1712 | 2300 | 0.4155 | - |
| 0.1749 | 2350 | 0.0143 | - |
| 0.1786 | 2400 | 0.0013 | - |
| 0.1824 | 2450 | 0.36 | - |
| 0.1861 | 2500 | 0.2805 | - |
| 0.1898 | 2550 | 0.1571 | - |
| 0.1935 | 2600 | 0.0925 | - |
| 0.1972 | 2650 | 0.1762 | - |
| 0.2010 | 2700 | 0.2168 | - |
| 0.2047 | 2750 | 0.0002 | - |
| 0.2084 | 2800 | 0.0706 | - |
| 0.2121 | 2850 | 0.5384 | - |
| 0.2159 | 2900 | 0.0003 | - |
| 0.2196 | 2950 | 0.3476 | - |
| 0.2233 | 3000 | 0.0143 | - |
| 0.2270 | 3050 | 0.0052 | - |
| 0.2307 | 3100 | 0.1282 | - |
| 0.2345 | 3150 | 0.0004 | - |
| 0.2382 | 3200 | 0.0165 | - |
| 0.2419 | 3250 | 0.0077 | - |
| 0.2456 | 3300 | 0.011 | - |
| 0.2493 | 3350 | 0.0098 | - |
| 0.2531 | 3400 | 0.0104 | - |
| 0.2568 | 3450 | 0.0378 | - |
| 0.2605 | 3500 | 0.0294 | - |
| 0.2642 | 3550 | 0.1213 | - |
| 0.2680 | 3600 | 0.0 | - |
| 0.2717 | 3650 | 0.0021 | - |
| 0.2754 | 3700 | 0.0017 | - |
| 0.2791 | 3750 | 0.0273 | - |
| 0.2828 | 3800 | 0.012 | - |
| 0.2866 | 3850 | 0.008 | - |
| 0.2903 | 3900 | 0.0047 | - |
| 0.2940 | 3950 | 0.0034 | - |
| 0.2977 | 4000 | 0.0006 | - |
| 0.3015 | 4050 | 0.1756 | - |
| 0.3052 | 4100 | 0.1939 | - |
| 0.3089 | 4150 | 0.1627 | - |
| 0.3126 | 4200 | 0.0004 | - |
| 0.3163 | 4250 | 0.2098 | - |
| 0.3201 | 4300 | 0.002 | - |
| 0.3238 | 4350 | 0.2378 | - |
| 0.3275 | 4400 | 0.2552 | - |
| 0.3312 | 4450 | 0.0074 | - |
| 0.3349 | 4500 | 0.002 | - |
| 0.3387 | 4550 | 0.0152 | - |
| 0.3424 | 4600 | 0.0031 | - |
| 0.3461 | 4650 | 0.0684 | - |
| 0.3498 | 4700 | 0.0023 | - |
| 0.3536 | 4750 | 0.2301 | - |
| 0.3573 | 4800 | 0.0155 | - |
| 0.3610 | 4850 | 0.0774 | - |
| 0.3647 | 4900 | 0.0005 | - |
| 0.3684 | 4950 | 0.0013 | - |
| 0.3722 | 5000 | 0.055 | - |
| 0.3759 | 5050 | 0.006 | - |
| 0.3796 | 5100 | 0.0534 | - |
| 0.3833 | 5150 | 0.2006 | - |
| 0.3870 | 5200 | 0.2059 | - |
| 0.3908 | 5250 | 0.2467 | - |
| 0.3945 | 5300 | 0.0038 | - |
| 0.3982 | 5350 | 0.0004 | - |
| 0.4019 | 5400 | 0.0009 | - |
| 0.4057 | 5450 | 0.0002 | - |
| 0.4094 | 5500 | 0.2144 | - |
| 0.4131 | 5550 | 0.0623 | - |
| 0.4168 | 5600 | 0.0007 | - |
| 0.4205 | 5650 | 0.3073 | - |
| 0.4243 | 5700 | 0.0001 | - |
| 0.4280 | 5750 | 0.1286 | - |
| 0.4317 | 5800 | 0.179 | - |
| 0.4354 | 5850 | 0.2131 | - |
| 0.4392 | 5900 | 0.0005 | - |
| 0.4429 | 5950 | 0.1989 | - |
| 0.4466 | 6000 | 0.1981 | - |
| 0.4503 | 6050 | 0.0004 | - |
| 0.4540 | 6100 | 0.0001 | - |
| 0.4578 | 6150 | 0.4378 | - |
| 0.4615 | 6200 | 0.0008 | - |
| 0.4652 | 6250 | 0.1022 | - |
| 0.4689 | 6300 | 0.0002 | - |
| 0.4726 | 6350 | 0.0648 | - |
| 0.4764 | 6400 | 0.2756 | - |
| 0.4801 | 6450 | 0.1552 | - |
| 0.4838 | 6500 | 0.0524 | - |
| 0.4875 | 6550 | 0.2472 | - |
| 0.4913 | 6600 | 0.3239 | - |
| 0.4950 | 6650 | 0.1255 | - |
| 0.4987 | 6700 | 0.0293 | - |
| 0.5024 | 6750 | 0.0 | - |
| 0.5061 | 6800 | 0.001 | - |
| 0.5099 | 6850 | 0.0008 | - |
| 0.5136 | 6900 | 0.2881 | - |
| 0.5173 | 6950 | 0.0002 | - |
| 0.5210 | 7000 | 0.0008 | - |
| 0.5247 | 7050 | 0.1938 | - |
| 0.5285 | 7100 | 0.0965 | - |
| 0.5322 | 7150 | 0.1608 | - |
| 0.5359 | 7200 | 0.088 | - |
| 0.5396 | 7250 | 0.0003 | - |
| 0.5434 | 7300 | 0.0129 | - |
| 0.5471 | 7350 | 0.0027 | - |
| 0.5508 | 7400 | 0.0805 | - |
| 0.5545 | 7450 | 0.0059 | - |
| 0.5582 | 7500 | 0.2299 | - |
| 0.5620 | 7550 | 0.0042 | - |
| 0.5657 | 7600 | 0.0097 | - |
| 0.5694 | 7650 | 0.0 | - |
| 0.5731 | 7700 | 0.1738 | - |
| 0.5769 | 7750 | 0.0002 | - |
| 0.5806 | 7800 | 0.0003 | - |
| 0.5843 | 7850 | 0.0 | - |
| 0.5880 | 7900 | 0.0889 | - |
| 0.5917 | 7950 | 0.0769 | - |
| 0.5955 | 8000 | 0.0003 | - |
| 0.5992 | 8050 | 0.0 | - |
| 0.6029 | 8100 | 0.0003 | - |
| 0.6066 | 8150 | 0.0 | - |
| 0.6103 | 8200 | 0.0 | - |
| 0.6141 | 8250 | 0.0008 | - |
| 0.6178 | 8300 | 0.0002 | - |
| 0.6215 | 8350 | 0.0001 | - |
| 0.6252 | 8400 | 0.0004 | - |
| 0.6290 | 8450 | 0.0003 | - |
| 0.6327 | 8500 | 0.0052 | - |
| 0.6364 | 8550 | 0.1168 | - |
| 0.6401 | 8600 | 0.0029 | - |
| 0.6438 | 8650 | 0.0004 | - |
| 0.6476 | 8700 | 0.0003 | - |
| 0.6513 | 8750 | 0.0256 | - |
| 0.6550 | 8800 | 0.0473 | - |
| 0.6587 | 8850 | 0.0002 | - |
| 0.6624 | 8900 | 0.0001 | - |
| 0.6662 | 8950 | 0.0 | - |
| 0.6699 | 9000 | 0.0 | - |
| 0.6736 | 9050 | 0.0 | - |
| 0.6773 | 9100 | 0.1554 | - |
| 0.6811 | 9150 | 0.0002 | - |
| 0.6848 | 9200 | 0.037 | - |
| 0.6885 | 9250 | 0.0008 | - |
| 0.6922 | 9300 | 0.0 | - |
| 0.6959 | 9350 | 0.0247 | - |
| 0.6997 | 9400 | 0.0 | - |
| 0.7034 | 9450 | 0.2489 | - |
| 0.7071 | 9500 | 0.0266 | - |
| 0.7108 | 9550 | 0.0002 | - |
| 0.7146 | 9600 | 0.0001 | - |
| 0.7183 | 9650 | 0.029 | - |
| 0.7220 | 9700 | 0.0 | - |
| 0.7257 | 9750 | 0.0151 | - |
| 0.7294 | 9800 | 0.1482 | - |
| 0.7332 | 9850 | 0.023 | - |
| 0.7369 | 9900 | 0.0 | - |
| 0.7406 | 9950 | 0.0005 | - |
| 0.7443 | 10000 | 0.1778 | - |
| 0.7480 | 10050 | 0.0002 | - |
| 0.7518 | 10100 | 0.0002 | - |
| 0.7555 | 10150 | 0.0 | - |
| 0.7592 | 10200 | 0.0709 | - |
| 0.7629 | 10250 | 0.2704 | - |
| 0.7667 | 10300 | 0.3767 | - |
| 0.7704 | 10350 | 0.0 | - |
| 0.7741 | 10400 | 0.0177 | - |
| 0.7778 | 10450 | 0.0944 | - |
| 0.7815 | 10500 | 0.0421 | - |
| 0.7853 | 10550 | 0.0001 | - |
| 0.7890 | 10600 | 0.0001 | - |
| 0.7927 | 10650 | 0.0001 | - |
| 0.7964 | 10700 | 0.0003 | - |
| 0.8001 | 10750 | 0.0 | - |
| 0.8039 | 10800 | 0.0001 | - |
| 0.8076 | 10850 | 0.0366 | - |
| 0.8113 | 10900 | 0.0277 | - |
| 0.8150 | 10950 | 0.0 | - |
| 0.8188 | 11000 | 0.0412 | - |
| 0.8225 | 11050 | 0.0001 | - |
| 0.8262 | 11100 | 0.0003 | - |
| 0.8299 | 11150 | 0.0 | - |
| 0.8336 | 11200 | 0.0016 | - |
| 0.8374 | 11250 | 0.059 | - |
| 0.8411 | 11300 | 0.0 | - |
| 0.8448 | 11350 | 0.0001 | - |
| 0.8485 | 11400 | 0.0002 | - |
| 0.8523 | 11450 | 0.0001 | - |
| 0.8560 | 11500 | 0.0001 | - |
| 0.8597 | 11550 | 0.1203 | - |
| 0.8634 | 11600 | 0.0261 | - |
| 0.8671 | 11650 | 0.0002 | - |
| 0.8709 | 11700 | 0.245 | - |
| 0.8746 | 11750 | 0.0 | - |
| 0.8783 | 11800 | 0.0 | - |
| 0.8820 | 11850 | 0.0002 | - |
| 0.8857 | 11900 | 0.0318 | - |
| 0.8895 | 11950 | 0.0232 | - |
| 0.8932 | 12000 | 0.0 | - |
| 0.8969 | 12050 | 0.0 | - |
| 0.9006 | 12100 | 0.0264 | - |
| 0.9044 | 12150 | 0.025 | - |
| 0.9081 | 12200 | 0.0152 | - |
| 0.9118 | 12250 | 0.0 | - |
| 0.9155 | 12300 | 0.0001 | - |
| 0.9192 | 12350 | 0.0 | - |
| 0.9230 | 12400 | 0.02 | - |
| 0.9267 | 12450 | 0.0073 | - |
| 0.9304 | 12500 | 0.1577 | - |
| 0.9341 | 12550 | 0.0207 | - |
| 0.9378 | 12600 | 0.0289 | - |
| 0.9416 | 12650 | 0.0001 | - |
| 0.9453 | 12700 | 0.0778 | - |
| 0.9490 | 12750 | 0.0712 | - |
| 0.9527 | 12800 | 0.0 | - |
| 0.9565 | 12850 | 0.0 | - |
| 0.9602 | 12900 | 0.0 | - |
| 0.9639 | 12950 | 0.0002 | - |
| 0.9676 | 13000 | 0.0 | - |
| 0.9713 | 13050 | 0.0001 | - |
| 0.9751 | 13100 | 0.0 | - |
| 0.9788 | 13150 | 0.0 | - |
| 0.9825 | 13200 | 0.1664 | - |
| 0.9862 | 13250 | 0.0014 | - |
| 0.9900 | 13300 | 0.1693 | - |
| 0.9937 | 13350 | 0.0264 | - |
| 0.9974 | 13400 | 0.0027 | - |
### Framework Versions
- Python: 3.10.13
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- spaCy: 3.7.5
- Transformers: 4.36.2
- PyTorch: 2.1.2
- Datasets: 2.19.2
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```